Language Translation with RNN
Max Lee & Yong Sheng
Term 7, 2022
References
Guide: https://github.com/tommytracey/AIND-Capstone https://tommytracey.github.io/AIND-Capstone/machine_translation.html
Why TimeDistributedDenseLayer: https://datascience.stackexchange.com/questions/10836/the-difference-between-dense-and-timedistributeddense-of-keras
Keras Documentation: https://tensorflow.rstudio.com/reference/keras/
Stackoverflow: https://stackoverflow.com/questions/10961141/setting-up-a-3d-matrix-in-r-and-accessing-certain-elements
Dataset: http://www.manythings.org/anki/
Attempt to train words using 8-10 Words accuracy could be due to PADDING
Importing of Libraries
library(deepviz)
language <- "French"
language_code <- "fr"
file_name <- paste0("translation_", language_code, ".csv")
train <- read.csv(file_name, encoding="UTF-8", stringsAsFactors=FALSE)# language <- "Indonesian"
# language_code <- "ind"
# file_name <- paste0("translation_", language_code, ".csv")
# train <- read.csv(file_name, encoding="UTF-8", stringsAsFactors=FALSE)Amending column names
colnames(train) <- c("English", language)trainTokenizer
tokenize <- function(x){
tokenizer <- text_tokenizer(num_words = 1000000)
fit_text_tokenizer(tokenizer, x)
sequences <- texts_to_sequences(tokenizer, x)
return(c(sequences, tokenizer))
}Padding
pad <- function(x, length=NULL){
return(pad_sequences(x, maxlen = length, padding = 'post'))
}Subsetting to 8-10 words within the English sentence
Finding number of words within an sentence
# sentences_length_vec <- function(word_list){
# output <- tokenize(word_list)
# sentence_length <- c()
# for(i in 1:length(word_list)){
# sentence_length[i] <- length(output[[i]])
# }
#
# sentence_length
# }
#
# english_sentence_length <- sentences_length_vec(list(train[, 1])[[1]])
# other_sentence_length <- sentences_length_vec(list(train[, 2])[[1]])
#
#
# ## Adding each sentence length to the dataframe `train`
# train$english_length <- english_sentence_length
# train$other_length <- other_sentence_length
#
# tail(train)
Conducting the subset of the dataframe
# lower_bound_words <- 8; upper_bound_words <- 10
# subset_train <- subset(train,
# train$english_length >= lower_bound_words & train$english_length <= upper_bound_words
# & train$other_length >= lower_bound_words & train$other_length <= upper_bound_words
# )
#
# ## Checking for the number of rows within the new subsetted dataframe for testing purposes.
# head(subset_train)
# tail(subset_train)
# nrow(subset_train)Example for Tokenisation & Padding
text_sentences = c('The quick brown fox jumps over the lazy dog .',
'By Jove , my quick study of lexicography won a prize .',
'This is a short sentence .')
token_index <- length(text_sentences) + 1
output <- tokenize(text_sentences)Loaded Tensorflow version 2.8.0text_tokenized <- output[1:length(text_sentences)]
# print(output)
# Finding out the integer allocation to each word
tk <- output[[token_index]]$word_index
# print(tk)
# print(length(tk))
# print(table(tk))Seeing the input vs output for each tokenized sentences
for(i in 1:length(text_sentences)){
print(paste0("Sequence in Text ", i, ":"))
print(paste0("Input: ", text_sentences[i]))
print(paste0("Output: ", list(text_tokenized[[i]])))
cat("\n")
}[1] "Sequence in Text 1:"
[1] "Input: The quick brown fox jumps over the lazy dog ."
[1] "Output: c(1, 2, 4, 5, 6, 7, 1, 8, 9)"
[1] "Sequence in Text 2:"
[1] "Input: By Jove , my quick study of lexicography won a prize ."
[1] "Output: c(10, 11, 12, 2, 13, 14, 15, 16, 3, 17)"
[1] "Sequence in Text 3:"
[1] "Input: This is a short sentence ."
[1] "Output: c(18, 19, 3, 20, 21)"Padding each tokenized sentences
# padded_text <- pad(text_tokenized)
# for(i in 1:length(text_sentences)){
# print(paste0("Sequence in Text ", i, ":"))
# print(paste0("Input: ", text_sentences[i]))
# print(paste0("Output: ", list(text_tokenized[[i]])))
# print(paste0("Output (Padded): ", list(padded_text[i,])))
# }Preprocessing Component (Tidying up of characters and sentences)
Getting Compiled English Text (Testing)
for(i in 1:n){
# if(i %% 100 != 0) next
print(paste0("Sequence in Text ", i, ":"))
print(paste0("Input: ", word_list[i]))
print(paste0("Output: ", list(new_text_tokenized[[i]])))
print(paste0("Output (Padded): ", list(new_padded_text[i,])))
cat("\n")
}
[1] "Sequence in Text 1:"
[1] "Input: new jersey est parfois calme pendant l' automne , et il est neigeux en avril ."
[1] "Output: c(15, 16, 1, 6, 7, 17, 18, 19, 3, 4, 1, 20, 2, 21)"
[1] "Output (Padded): c(15, 16, 1, 6, 7, 17, 18, 19, 3, 4, 1, 20, 2, 21)"
[1] "Sequence in Text 2:"
[1] "Input: les états-unis est généralement froid en juillet , et il gèle habituellement en novembre ."
[1] "Output: c(8, 9, 10, 1, 5, 11, 2, 22, 3, 4, 23, 24, 2, 25)"
[1] "Output (Padded): c(8, 9, 10, 1, 5, 11, 2, 22, 3, 4, 23, 24, 2, 25)"
[1] "Sequence in Text 3:"
[1] "Input: california est généralement calme en mars , et il est généralement chaud en juin ."
[1] "Output: c(26, 1, 5, 7, 2, 27, 3, 4, 1, 5, 28, 2, 12)"
[1] "Output (Padded): c(26, 1, 5, 7, 2, 27, 3, 4, 1, 5, 28, 2, 12, 0)"
[1] "Sequence in Text 4:"
[1] "Input: les états-unis est parfois légère en juin , et il fait froid en septembre ."
[1] "Output: c(8, 9, 10, 1, 6, 29, 2, 12, 3, 4, 30, 11, 2, 31)"
[1] "Output (Padded): c(8, 9, 10, 1, 6, 29, 2, 12, 3, 4, 30, 11, 2, 31)"
[1] "Sequence in Text 5:"
[1] "Input: votre moins aimé fruit est le raisin , mais mon moins aimé est la pomme ."
[1] "Output: c(32, 13, 14, 33, 1, 34, 35, 36, 37, 13, 14, 1, 38, 39)"
[1] "Output (Padded): c(32, 13, 14, 33, 1, 34, 35, 36, 37, 13, 14, 1, 38, 39)"
[1] "Sequence in Text 6:"
[1] "Input: NA"Error in new_text_tokenized[[i]] : subscript out of boundsGetting Compiled Other Language Text (Testing)
# n <- nrow(subset_train)
n <- 5
word_list <- list(train[, 2])[[1]][1:n]
new_output <- tokenize(word_list)
new_text_tokenized <- new_output[1:n]
new_padded_text <- pad(new_text_tokenized)
for(i in 1:n){
# if(i %% 100 != 0) next
print(paste0("Sequence in Text ", i, ":"))
print(paste0("Input: ", word_list[i]))
print(paste0("Output: ", list(new_text_tokenized[[i]])))
print(paste0("Output (Padded): ", list(new_padded_text[i,])))
}[1] "Sequence in Text 1:"
[1] "Input: new jersey est parfois calme pendant l' automne , et il est neigeux en avril ."
[1] "Output: c(15, 16, 1, 6, 7, 17, 18, 19, 3, 4, 1, 20, 2, 21)"
[1] "Output (Padded): c(15, 16, 1, 6, 7, 17, 18, 19, 3, 4, 1, 20, 2, 21)"
[1] "Sequence in Text 2:"
[1] "Input: les états-unis est généralement froid en juillet , et il gèle habituellement en novembre ."
[1] "Output: c(8, 9, 10, 1, 5, 11, 2, 22, 3, 4, 23, 24, 2, 25)"
[1] "Output (Padded): c(8, 9, 10, 1, 5, 11, 2, 22, 3, 4, 23, 24, 2, 25)"
[1] "Sequence in Text 3:"
[1] "Input: california est généralement calme en mars , et il est généralement chaud en juin ."
[1] "Output: c(26, 1, 5, 7, 2, 27, 3, 4, 1, 5, 28, 2, 12)"
[1] "Output (Padded): c(26, 1, 5, 7, 2, 27, 3, 4, 1, 5, 28, 2, 12, 0)"
[1] "Sequence in Text 4:"
[1] "Input: les états-unis est parfois légère en juin , et il fait froid en septembre ."
[1] "Output: c(8, 9, 10, 1, 6, 29, 2, 12, 3, 4, 30, 11, 2, 31)"
[1] "Output (Padded): c(8, 9, 10, 1, 6, 29, 2, 12, 3, 4, 30, 11, 2, 31)"
[1] "Sequence in Text 5:"
[1] "Input: votre moins aimé fruit est le raisin , mais mon moins aimé est la pomme ."
[1] "Output: c(32, 13, 14, 33, 1, 34, 35, 36, 37, 13, 14, 1, 38, 39)"
[1] "Output (Padded): c(32, 13, 14, 33, 1, 34, 35, 36, 37, 13, 14, 1, 38, 39)"Preprocessing both languages compilations
preprocess_text <- function(x, y){
output_x <- tokenize(x)
output_y <- tokenize(y)
preprocess_x <- output_x[1:length(x)]; x_tk <- output_x[[length(x) + 1]]$word_index
preprocess_y <- output_y[1:length(y)]; y_tk <- output_y[[length(y) + 1]]$word_index
# print(preprocess_x)
preprocess_x <- pad(preprocess_x)
preprocess_y <- pad(preprocess_y)
# print(preprocess_x)
# Converting from a 2D matrix to a 3D tensor
# preprocess_x <- array(preprocess_x[[1]], c(dim(preprocess_x[[1]])[1], dim(preprocess_x[[1]])[2], 1))
# preprocess_y <- array(preprocess_y[[1]], c(dim(preprocess_y[[1]])[1], dim(preprocess_y[[1]])[2], 1))
return(list(preprocess_x, preprocess_y, x_tk, y_tk))
}Full Data
train_x <- list(train[, 1])[[1]]
train_y <- list(train[, 2])[[1]]
# print(subset_train_x)
process_output <- preprocess_text(train_x, train_y)
# print(process_output[4],)
preprocess_x <- process_output[1]; preprocess_y <- process_output[2]; x_tk <- process_output[3]; y_tk <- process_output[4]
# print(preprocess_x[[1]])
# print(preprocess_y[[1]])
# Conversion back to list of words from tokenized word list
# attributes(x_tk[[1]])$names
# length(y_tk[[1]])Subset data
# n <- nrow(train) #1000
# subset_train_x <- list(subset_train[, 1])[[1]][1:n]
# subset_train_y <- list(subset_train[, 2])[[1]][1:n]
# # print(subset_train_x)
#
# process_output <- preprocess_text(subset_train_x, subset_train_y)
# # print(process_output[4],)
# preprocess_x <- process_output[1]; preprocess_y <- process_output[2]; x_tk <- process_output[3]; y_tk <- process_output[4]
# # print(preprocess_x[[1]])
# # print(preprocess_y[[1]])
#
#
# # Conversion back to list of words from tokenized word list
# # attributes(x_tk[[1]])$names
# # length(y_tk[[1]])Obtaining the maximum column number and re-padding
col_x <- dim(preprocess_x[[1]])[2]
col_y <- dim(preprocess_y[[1]])[2]
if(col_x >= col_y){
max_col <- col_x
}else{
max_col <- col_y
}
tmp_x <- pad(preprocess_x[[1]], max_col)
tmp_y <- pad(preprocess_y[[1]], max_col)
Checking Correspondance between subset_train and tmp
row <- 5
head(tmp_x) [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21]
[1,] 17 23 1 8 67 4 39 7 3 1 55 2 44 0 0 0 0 0 0 0 0
[2,] 5 20 21 1 9 62 4 43 7 3 1 9 51 2 45 0 0 0 0 0 0
[3,] 22 1 9 67 4 38 7 3 1 9 68 2 34 0 0 0 0 0 0 0 0
[4,] 5 20 21 1 8 64 4 34 7 3 1 57 2 42 0 0 0 0 0 0 0
[5,] 29 12 16 13 1 5 82 6 30 12 16 1 5 83 0 0 0 0 0 0 0
[6,] 31 11 13 1 5 84 6 30 11 1 5 82 0 0 0 0 0 0 0 0 0train_x[row][1] "your least liked fruit is the grape , but my least liked is the apple ."Calculating Sparsity
calculate_sparsity <- function(df_matrix){
zero_count <- 0
total_count <- nrow(df_matrix) * ncol(tmp_x)
for(i in 1:nrow(df_matrix)){
for(j in 1:ncol(df_matrix)){
if(df_matrix[i, j] == 0){
zero_count = zero_count + 1
}
}
}
zero_count/total_count
}
print(paste("The Sparsity of the matrix is: ", round(calculate_sparsity(tmp_x)*100, 2), "%"))[1] "The Sparsity of the matrix is: 46.37 %"Conversion of 2D matrix to tensor
convert2tensor <- function(preprocess_data){
preprocess_data <- array(preprocess_data, c(dim(preprocess_data)[1], dim(preprocess_data)[2], 1))
return(preprocess_data)
}
# array(preprocess_x[[1]], c(dim(preprocess_x[[1]])[1], dim(preprocess_x[[1]])[2], 1))
# dim(array(preprocess_x[[1]], c(dim(preprocess_x[[1]])[1], dim(preprocess_x[[1]])[2], 1)))[2:3]Converting to tensor
tensor_x <- convert2tensor(tmp_x)
dim(tensor_x)[1] 137860 21 1tensor_x[1, , ] [1] 17 23 1 8 67 4 39 7 3 1 55 2 44 0 0 0 0 0 0 0 0tensor_y <- convert2tensor(tmp_y)
# tensor_yConverting the logits back to text
logits_to_text <- function(logits, tokenizer, predict=FALSE){
tokenizer_words <- attributes(tokenizer[[1]])$names
text <- c()
if(predict == TRUE){
logits <- logits - 1 ## For prediction conversion only
}
for(i in logits){
if(i == 0){
text <- c(text, "<PAD>")
}else{
text <- c(text, tokenizer_words[i])
}
}
return(text)
}
# Testing to convert the first row back to text
# preprocess_x[[1]][1, ]
# preprocess_x[[1]]
logits_to_text(preprocess_x[[1]][1, ], x_tk) [1] "new" "jersey" "is" "sometimes" "quiet" "during" "autumn" "and" "it" "is" "snowy"
[12] "in" "april" "<PAD>" "<PAD>" Building a simple RNN model
history = model_RNN %>% fit(
x = tensor_x, y = tensor_y,
epochs = 10,
batch_size = 1024,
validation_split = 0.2,
)Epoch 1/10
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108/108 [==============================] - 67s 615ms/step - loss: 1.7713 - accuracy: 0.5672 - val_loss: 1.2585 - val_accuracy: 0.6358
Epoch 2/10
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108/108 [==============================] - 65s 604ms/step - loss: 1.2232 - accuracy: 0.6423
108/108 [==============================] - 70s 649ms/step - loss: 1.2232 - accuracy: 0.6423 - val_loss: 1.1022 - val_accuracy: 0.6666
Epoch 3/10
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108/108 [==============================] - 66s 608ms/step - loss: 1.1041 - accuracy: 0.6608
108/108 [==============================] - 70s 653ms/step - loss: 1.1041 - accuracy: 0.6608 - val_loss: 1.0046 - val_accuracy: 0.6781
Epoch 4/10
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108/108 [==============================] - 66s 607ms/step - loss: 1.0366 - accuracy: 0.6710
108/108 [==============================] - 70s 653ms/step - loss: 1.0366 - accuracy: 0.6710 - val_loss: 0.9434 - val_accuracy: 0.6911
Epoch 5/10
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108/108 [==============================] - 67s 625ms/step - loss: 0.9995 - accuracy: 0.6770
108/108 [==============================] - 72s 670ms/step - loss: 0.9995 - accuracy: 0.6770 - val_loss: 0.9169 - val_accuracy: 0.6930
Epoch 6/10
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108/108 [==============================] - 66s 615ms/step - loss: 0.9589 - accuracy: 0.6855
108/108 [==============================] - 71s 660ms/step - loss: 0.9589 - accuracy: 0.6855 - val_loss: 0.8878 - val_accuracy: 0.7037
Epoch 7/10
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108/108 [==============================] - 67s 616ms/step - loss: 0.9394 - accuracy: 0.6884
108/108 [==============================] - 71s 662ms/step - loss: 0.9394 - accuracy: 0.6884 - val_loss: 0.8578 - val_accuracy: 0.7032
Epoch 8/10
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108/108 [==============================] - 67s 617ms/step - loss: 0.9072 - accuracy: 0.6945
108/108 [==============================] - 71s 662ms/step - loss: 0.9072 - accuracy: 0.6945 - val_loss: 0.8263 - val_accuracy: 0.7175
Epoch 9/10
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108/108 [==============================] - 67s 620ms/step - loss: 0.8940 - accuracy: 0.6965
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Epoch 10/10
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108/108 [==============================] - 74s 684ms/step - loss: 0.8788 - accuracy: 0.6983 - val_loss: 0.8124 - val_accuracy: 0.7149plot(history)`geom_smooth()` using formula 'y ~ x'
Prediction
Obtaining the output for prediction (Testing)
predict_output <- model_RNN %>% predict(matrix(tensor_x[5, ,], nrow=1))
# predict_output
predict_output <- argmax(predict_output, FALSE)
# train_x[5]
train_y[5][1] "votre moins aimé fruit est le raisin , mais mon moins aimé est la pomme ."logits_to_text(predict_output, y_tk, predict = TRUE) [1] "votre" "fruit" "est" "moins" "aimé" "la" "mais" "mais" "mon" "moins" "aimé" "est" "la" "citron" "<PAD>"
[16] "<PAD>" "<PAD>" "<PAD>" "<PAD>" "<PAD>" "<PAD>" Predictions for Training set
pred_translation <- function(i){
predict_output <- model_RNN %>% predict(matrix(tensor_x[i, ,], nrow=1))
predict_output <- argmax(predict_output, FALSE)
converted_text <- logits_to_text(predict_output, y_tk)
converted_text[converted_text == "<PAD>"] <- ""
converted_text <- trimws(paste(converted_text, collapse = " "))
print(paste("Input sentence:", train_x[i]))
print(paste("Intended Output Sentence:", train_y[i]))
print(paste("Predicted Output Sentence:", converted_text))
}
## `i` represents the index within the training set.
pred_translation(5)[1] "Input sentence: your least liked fruit is the grape , but my least liked is the apple ."
[1] "Intended Output Sentence: votre moins aimé fruit est le raisin , mais mon moins aimé est la pomme ."
[1] "Predicted Output Sentence: votre fruit est moins aimé la mais mais mon moins aimé est la citron"---
title: "Language Translation with RNN"
author: "Max Lee & Yong Sheng"
date: "Term 7, 2022"
output: html_notebook
---

*References*

Guide:
https://github.com/tommytracey/AIND-Capstone
https://tommytracey.github.io/AIND-Capstone/machine_translation.html

Why TimeDistributedDenseLayer:
https://datascience.stackexchange.com/questions/10836/the-difference-between-dense-and-timedistributeddense-of-keras

Keras Documentation:
https://tensorflow.rstudio.com/reference/keras/

Stackoverflow:
https://stackoverflow.com/questions/10961141/setting-up-a-3d-matrix-in-r-and-accessing-certain-elements

Dataset:
http://www.manythings.org/anki/


*Attempt to train words using 8-10 Words* accuracy could be due to PADDING

# Importing of Libraries
```{r warning=FALSE,results='hide',error=FALSE,message=FALSE}
library(keras)
library(tensorflow)
library(tokenizers)
library(dplyr)
library(png)
library(reticulate)
library(abind)
library(ramify)
library(stringr)
library(deepviz)
```

```{r}
language <- "French"
language_code <- "fr"
file_name <- paste0("translation_", language_code, ".csv")
train <- read.csv(file_name, encoding="UTF-8", stringsAsFactors=FALSE)
```

```{r}
# language <- "Indonesian"
# language_code <- "ind"
# file_name <- paste0("translation_", language_code, ".csv")
# train <- read.csv(file_name, encoding="UTF-8", stringsAsFactors=FALSE)
```
## Amending column names
```{r}
colnames(train) <- c("English", language)
```

```{r}
train
```
# Tokenizer
```{r}
tokenize <- function(x){
  tokenizer <- text_tokenizer(num_words = 1000000)
  fit_text_tokenizer(tokenizer, x)
  sequences <- texts_to_sequences(tokenizer, x)
  return(c(sequences, tokenizer))
}
```

# Padding
```{r}
pad <- function(x, length=NULL){
  return(pad_sequences(x, maxlen = length, padding = 'post'))
}
```

# Subsetting to 8-10 words within the English sentence

## Finding number of words within an  sentence
```{r}
# sentences_length_vec <- function(word_list){
#   output <- tokenize(word_list)
#   sentence_length <- c()
#   for(i in 1:length(word_list)){
#     sentence_length[i] <- length(output[[i]])
#   }
#   
#   sentence_length
# }
# 
# english_sentence_length <- sentences_length_vec(list(train[, 1])[[1]])
# other_sentence_length <- sentences_length_vec(list(train[, 2])[[1]])
# 
# 
# ## Adding each sentence length to the dataframe `train`
# train$english_length <- english_sentence_length 
# train$other_length <- other_sentence_length
# 
# tail(train)

```

## Conducting the subset of the dataframe
```{r}
# lower_bound_words <- 8; upper_bound_words <- 10 
# subset_train <- subset(train, 
#                        train$english_length >= lower_bound_words & train$english_length <= upper_bound_words 
#                        & train$other_length >= lower_bound_words & train$other_length <= upper_bound_words
#                          )
# 
# ## Checking for the number of rows within the new subsetted dataframe for testing purposes.
# head(subset_train)
# tail(subset_train)
# nrow(subset_train)
```




# Example for Tokenisation & Padding
```{r}
text_sentences = c('The quick brown fox jumps over the lazy dog .',
    'By Jove , my quick study of lexicography won a prize .',
    'This is a short sentence .')
token_index <- length(text_sentences) + 1
output <- tokenize(text_sentences)
text_tokenized <- output[1:length(text_sentences)]
# print(output)

# Finding out the integer allocation to each word
tk <- output[[token_index]]$word_index
# print(tk)
# print(length(tk))
# print(table(tk))
```
## Seeing the input vs output for each tokenized sentences
```{r}
for(i in 1:length(text_sentences)){
  print(paste0("Sequence in Text ", i, ":"))
  print(paste0("Input: ", text_sentences[i]))
  print(paste0("Output: ", list(text_tokenized[[i]])))
  cat("\n")
}
```

## Padding each tokenized sentences
```{r}
# padded_text <- pad(text_tokenized)
# for(i in 1:length(text_sentences)){
#   print(paste0("Sequence in Text ", i, ":"))
#   print(paste0("Input: ", text_sentences[i]))
#   print(paste0("Output: ", list(text_tokenized[[i]])))
#   print(paste0("Output (Padded): ", list(padded_text[i,])))
# }
```



# Preprocessing Component (Tidying up of characters and sentences)

## Getting Compiled English Text (Testing)
```{r}
# n <- nrow(subset_train)
n <- 5
word_list <- list(train[, 1])[[1]][1:n]
# word_list
new_output <- tokenize(word_list)
new_text_tokenized <- new_output[1:n]
new_padded_text <- pad(new_text_tokenized)

for(i in 1:n){
  # if(i %% 100 != 0) next
  print(paste0("Sequence in Text ", i, ":"))
  print(paste0("Input: ", word_list[i]))
  print(paste0("Output: ", list(new_text_tokenized[[i]])))
  print(paste0("Output (Padded): ", list(new_padded_text[i,])))
  cat("\n")
}

```

## Getting Compiled Other Language Text (Testing)
```{r}
# n <- nrow(subset_train)
n <- 5
word_list <- list(train[, 2])[[1]][1:n]
new_output <- tokenize(word_list)
new_text_tokenized <- new_output[1:n]
new_padded_text <- pad(new_text_tokenized)

for(i in 1:n){
  # if(i %% 100 != 0) next
  print(paste0("Sequence in Text ", i, ":"))
  print(paste0("Input: ", word_list[i]))
  print(paste0("Output: ", list(new_text_tokenized[[i]])))
  print(paste0("Output (Padded): ", list(new_padded_text[i,])))
}

```


## Preprocessing both languages compilations
```{r}
preprocess_text <- function(x, y){
  output_x <- tokenize(x)
  output_y <- tokenize(y)
  
  preprocess_x <- output_x[1:length(x)]; x_tk <- output_x[[length(x) + 1]]$word_index
  preprocess_y <- output_y[1:length(y)]; y_tk <- output_y[[length(y) + 1]]$word_index
  
  # print(preprocess_x)
  
  preprocess_x <- pad(preprocess_x)
  preprocess_y <- pad(preprocess_y)
  
  # print(preprocess_x)
  
  # Converting from a 2D matrix to a 3D tensor
  # preprocess_x <- array(preprocess_x[[1]], c(dim(preprocess_x[[1]])[1], dim(preprocess_x[[1]])[2], 1))
  # preprocess_y <- array(preprocess_y[[1]], c(dim(preprocess_y[[1]])[1], dim(preprocess_y[[1]])[2], 1))
  
  return(list(preprocess_x, preprocess_y, x_tk, y_tk))
}
```

## Full Data
```{r}
train_x <- list(train[, 1])[[1]]
train_y <- list(train[, 2])[[1]]
# print(subset_train_x)

process_output <- preprocess_text(train_x, train_y)
# print(process_output[4],)
preprocess_x <- process_output[1]; preprocess_y <- process_output[2]; x_tk <- process_output[3]; y_tk <- process_output[4]
# print(preprocess_x[[1]])
# print(preprocess_y[[1]])


# Conversion back to list of words from tokenized word list
# attributes(x_tk[[1]])$names
# length(y_tk[[1]])
```


## Subset data
```{r}
# n <- nrow(train) #1000
# subset_train_x <- list(subset_train[, 1])[[1]][1:n]
# subset_train_y <- list(subset_train[, 2])[[1]][1:n]
# # print(subset_train_x)
# 
# process_output <- preprocess_text(subset_train_x, subset_train_y)
# # print(process_output[4],)
# preprocess_x <- process_output[1]; preprocess_y <- process_output[2]; x_tk <- process_output[3]; y_tk <- process_output[4]
# # print(preprocess_x[[1]])
# # print(preprocess_y[[1]])
# 
# 
# # Conversion back to list of words from tokenized word list
# # attributes(x_tk[[1]])$names
# # length(y_tk[[1]])
```

# Obtaining the maximum column number and re-padding
```{r}
col_x <- dim(preprocess_x[[1]])[2]
col_y <- dim(preprocess_y[[1]])[2]

if(col_x >= col_y){
  max_col <- col_x
}else{
  max_col <- col_y
}

tmp_x <- pad(preprocess_x[[1]], max_col)
tmp_y <- pad(preprocess_y[[1]], max_col)


```

# Checking Correspondance between subset_train and tmp
```{r}
row <- 5
head(tmp_x)
train_x[row]
```
## Calculating Sparsity
```{r}
calculate_sparsity <- function(df_matrix){
  zero_count <- 0
  total_count <- nrow(df_matrix) * ncol(tmp_x)
  for(i in 1:nrow(df_matrix)){
    for(j in 1:ncol(df_matrix)){
      if(df_matrix[i, j] == 0){
        zero_count = zero_count + 1
      }
    }
  }
  zero_count/total_count
}

print(paste("The Sparsity of the matrix is: ", round(calculate_sparsity(tmp_x)*100, 2), "%"))
```



# Conversion of 2D matrix to tensor
```{r}
convert2tensor <- function(preprocess_data){
  preprocess_data <- array(preprocess_data, c(dim(preprocess_data)[1], dim(preprocess_data)[2], 1))
  return(preprocess_data)
}

# array(preprocess_x[[1]], c(dim(preprocess_x[[1]])[1], dim(preprocess_x[[1]])[2], 1))
# dim(array(preprocess_x[[1]], c(dim(preprocess_x[[1]])[1], dim(preprocess_x[[1]])[2], 1)))[2:3]
```


# Converting to tensor
```{r}
tensor_x <- convert2tensor(tmp_x)
dim(tensor_x)
tensor_x[1, , ]
tensor_y <- convert2tensor(tmp_y)
# tensor_y
```



# Converting the logits back to text
```{r}

logits_to_text <- function(logits, tokenizer, predict=FALSE){
  tokenizer_words <- attributes(tokenizer[[1]])$names
  text <- c()
  if(predict == TRUE){
    logits <- logits - 1 ## For prediction conversion only 
  }
  for(i in logits){
    if(i == 0){
      text <- c(text, "<PAD>")
    }else{
      text <- c(text, tokenizer_words[i])
    }
  }
  return(text)
}

# Testing to convert the first row back to text
# preprocess_x[[1]][1, ]
# preprocess_x[[1]]
logits_to_text(preprocess_x[[1]][1, ], x_tk)

```


# Building a simple RNN model
```{r}
# dim(tensor_y)
model_RNN <-  keras_model_sequential()
model_RNN %>% 
  layer_simple_rnn(units = 256, input_shape = dim(tensor_x)[2:3], return_sequences = TRUE) %>%
  time_distributed(layer_dense(units = 1024, activation = 'relu'))%>%
  layer_dropout(rate = 0.5) %>%
  time_distributed(layer_dense(units = length(y_tk[[1]]) + 1, activation = 'softmax'))

model_RNN %>% summary()

model_RNN %>% compile(
  loss      = 'sparse_categorical_crossentropy',
  # optimizer = optimizer_rmsprop(),
  optimizer = optimizer_adam(learning_rate = 0.005),
  metrics=c('accuracy')
)

plot_model(model_RNN)
```
```{r}

history = model_RNN %>% fit(
  x = tensor_x, y = tensor_y,
  epochs           = 10,
  batch_size = 1024,
  validation_split = 0.2,
)
plot(history)
```


# Prediction


## Obtaining the output for prediction (Testing)
```{r}
predict_output <- model_RNN %>% predict(matrix(tensor_x[5, ,], nrow=1))
# predict_output


predict_output <- argmax(predict_output, FALSE)
# train_x[5]
train_y[5]
logits_to_text(predict_output, y_tk, predict = TRUE)

```
## Predictions for Training set
```{r}

pred_translation <- function(i){
  predict_output <- model_RNN %>% predict(matrix(tensor_x[i, ,], nrow=1))
  predict_output <- argmax(predict_output, FALSE)
  converted_text <- logits_to_text(predict_output, y_tk, predict = TRUE)
  converted_text[converted_text == "<PAD>"] <- ""
  converted_text <- trimws(paste(converted_text, collapse = " "))
  print(paste("Input sentence:", train_x[i]))
  print(paste("Intended Output Sentence:", train_y[i]))
  print(paste("Predicted Output Sentence:", converted_text))
}

## `i` represents the index within the training set.
pred_translation(5)

```

